The number of network parameters for the systems illustrated in Table REF  is shown in column 5 (where such numbers are available). The two best Raw PC-DARTS architectures have in excess of 24M parameters. For the Mel-Fixed configuration, 77% (18.89M) of the learnable network parameters correspond to GRU layers wereas only 18% (4.52M) correspond to the stacked cells. The RawNet2 system, which also uses a GRU, has over 25M parameters. Other systems have far fewer parameters, including the top Res-TSSDNet system which has 0.35M parameters. It uses ResNet-style 1D convolution blocks and 3 FC layers, without GRUs. The use of dilated convolutions helps to control network complexity while increasing the receptive field [1]}. Though the LCNN-LSTM-sum system uses two bidirectional LSTM layers, which is normally computationally expensive, use of a hidden size of 48 nonetheless means that the complexity is the lowest of all illustrated systems. The additional complexity of the Raw PC-DARTS architecture is currently a limitation in the approach, yet a compromise that might be acceptable given that learning and optimisation is a one-step process requiring comparatively little human effort.
